PPT-Learning Drivers through Imitation using Supervised Methods

By Luigi Cardamone Daniele Loiacono and Pier Luca Lanzi The outline Introduction Related work Torcs Imitation learning What sensors What actions What learning method What data. Ashwath Rajan. Overview, in brief. Marriage between statistics, linear algebra, calculus, and computer science. Machine Learning:. Supervised Learning. ex: linear Regression. Unsupervised Learning. ex: clustering. Introductions . Name. Department/Program. If research, what are you working on.. Your favorite fruit.. How do you estimate P(. y|x. ) . Types of Learning. Supervised Learning. Unsupervised Learning. Semi-supervised Learning. Yacine . Jernite. Text-as-Data series. September 17. 2015. What do we want from text?. Extract information. Link to other knowledge sources. Use knowledge (Wikipedia, . UpToDate,…). How do we answer those questions?. in Imitation and Social Learning in Robots, Humans and Animals, . Nehaniv and Dautenhaln. . Course: Robots Learning from Humans. Geonmo Gu. Computer Theory and Application Laboratory. School of Computer Science and Engineering. (2/2. ). in Imitation and Social Learning in Robots, Humans and Animals, . Nehaniv. & . Dautenhahn. Course: Robots Learning from Humans. Dong-. Kyoung. . Kye. 2015. 11. 13. Vehicle Intelligence Laboratory. Stephen . Billett. , Griffith University, Australia. Progression. Current project. Learning through circumstances of practice. Mimesis. Mimetic learning. Enacting and supporting mimetic learning through work. Introduction. Labelled data. Unlabeled data. cat. dog. (Image of cats and dogs without labeling). Introduction. Supervised learning: . E.g. . : image, . : class. . labels. Semi-supervised learning: . Vicarious Reinforcement Effects. Albert . Bandura. Observational or . Social Learning. Divided imitative behavior . into 3 categories. Same behavior. :. Copying behavior:. Matched-dependent behavior. Omer Levy. . Ido. Dagan. Bar-. Ilan. University. Israel. Steffen Remus Chris . Biemann. Technische. . Universität. Darmstadt. Germany. Lexical Inference. Lexical Inference: Task Definition. Learning What is learning? What are the types of learning? Why aren’t robots using neural networks all the time? They are like the brain, right? Where does learning go in our operational architecture? Andrea . Bertozzi. University of California, Los Angeles. Diffuse interface methods. Ginzburg-Landau functional. Total variation. W is a double well potential with two minima. Total variation measures length of boundary between two constant regions.. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The use of big data analytics in transaction banking – Dr. Martin Diehl. Discussant:. Adrian Guerin, Bank of Canada*. . *Any opinions expressed herein are those of the discussant and do not necessarily represent the views of the Bank of Canada.